sched and control of fms

Scheduling and Control of Flexible Manufacturing Systems :

Scheduling and Control of Flexible Manufacturing Systems Prepared by: Bopanna.K.D Under the guidance of Dr. G.Thangamani Professor Department Of Mechanical Engineering, Sir M.Visvesvaraya Institute Of Technology, Bangalore

INTRODUCTION:

INTRODUCTION Flexible manufacturing systems (FMS) are distinguished by the use of computer control in place of the hard automation usually found in transfer lines FMS provide significant advantages, including reduced work-in-process inventory, reduced throughput time, improved quality, and increased machine utilization

ONE OF THE MOST DIFFICULT PROBLEMS ARISING IN FLEXIBLE MANUFACTURING SYSTEMS IS SCHEDULING PROBLEM.:

ONE OF THE MOST DIFFICULT PROBLEMS ARISING IN FLEXIBLE MANUFACTURING SYSTEMS IS SCHEDULING PROBLEM. It May Be : Fabrication Assembly Machining

FMS PERFORMANCE STRONGLY DEPENDS ON THE SCHEDULING STRATEGIES USED.:

FMS PERFORMANCE STRONGLY DEPENDS ON THE SCHEDULING STRATEGIES USED. FMS scheduling problems are known to be hard and generally involve a large number of machines and part types. In addition, searching for an optimal schedule in a dynamic system, such as an FMS, may not be practical since it is too time-consuming to provide a quick response to real-time events.

OVERALL FMS PROBLEM ARE STRUCTURED AS ::

OVERALL FMS PROBLEM ARE STRUCTURED AS : Aggregate Scheduling Problem Upper Level Problem Real Time Scheduling Lower Level Problem

OVERALL SCHEDULING OBJECTIVES OF THE FMS:

OVERALL SCHEDULING OBJECTIVES OF THE FMS meet due dates, minimize work-in-process (WIP) inventory, minimize the average flow time of orders through the system, achieve high machine and worker time utilization, etc. balance the assigned machine processing times. balance the workload per machine for a system of groups of pooled machines of equal sizes.

DIFFERENT VIEWPOINTS OF OPERATIONAL PROBLEMS:

DIFFERENT VIEWPOINTS OF OPERATIONAL PROBLEMS Methodology used in resolving the problem Applications viewpoint Time horizon considered FMS factors considered

Methodology:

Methodology MATHEMATICAL PROGRAMMING APPROACH To manage the complexity of the problem, the FMS operation problem have divided into two sub problems: preproduction setup production operatio n.

AFTER THIS PREPARATORY PLANNING PHASE, THE REMAINING PROBLEMS ARE CALLED OPERATIONAL PROBLEMS:

AFTER THIS PREPARATORY PLANNING PHASE, THE REMAINING PROBLEMS ARE CALLED OPERATIONAL PROBLEMS 1 ) Part type selection problem. 2) Machine grouping problem. 3) Production ratio problem. 4) Resource allocation problem. 5) Loading problem.

MULTIPLE-CRITERIA DECISION MAKING APPROACH :

MULTIPLE-CRITERIA DECISION MAKING APPROACH meeting production requirements, balancing of machine utilization, minimization of throughput time of parts.

HEURISTICS ORIENTED APPROACH :

HEURISTICS ORIENTED APPROACH In the scheduling context, they report on three part sequencing situations: Initial entry of parts into an empty system, General entry of parts into a loaded system, Allocation of parts to machines within the system (dispatching rules)

CONTROL THEORETIC APPROACH :

CONTROL THEORETIC APPROACH A closed loop formulation of the FMS scheduling problem The closed loop control policy is tailored for a dedicated FMS producing a particular part mix. The tooling of the FMS, buffer capacity and other constraints are not considered. It is assumed that the input of a part is a sufficient control decision, and the (alternate) routing, possible deadlocks, blocking, etc. need not be considered. Further, the possible effect of long total processing times of parts in the FMS on the feedback loop is ignored

SIMULATION BASED APPROACH :

SIMULATION BASED APPROACH Recently some have presented discrete event simulation as a scheduling tool. Basically, simulation is proposed as a tool to evaluate the dispatching rules. The simulation model is initialized to the exact current state of the factory. The dispatching rules are then tested on this model .

ARTIFICIAL INTELLIGENCE BASED APPROACH :

ARTIFICIAL INTELLIGENCE BASED APPROACH Artificial intelligence (ai) appears to be particularly suited to solving operation problems of FMS -problems involving A large search space, and where human expertise can find reasonable solutions pretty fast. Many researchers have sought to utilize this similarity. So far, two techniques of ai have found use in the fms literature: Expert systems and planning. A nonlinear planning algorithm

HIERARCHICAL APPROACH :

HIERARCHICAL APPROACH Since an FMS has to achieve multiple performance objectives, a monolithic scheduling algorithm would be complex even if it is capable of addressing all objectives. It is achieved by the controllers at different levels of the hierarchical architecture, namely the shop level, and the cell level. The shop level controller employs a combined priority rule to rank shop orders considering multiple scheduling objectives.

FMS SCHEDULING CONSIDERATIONS  :

FMS SCHEDULING CONSIDERATIONS SINGLE RULE VERSUS MIXED RULES Since no single rule consistently outperforms all other rules, they use the mixed dispatching rule (MDR) approach in FMS scheduling by mixing four rules: next-in next out, shortest processing time (SPT), largest slack first, and first-in first-out Due to the interchangeability of machines, a part can have several alternative operations. To avoid long queues, an alternative operation may be used, often performed by machines of lesser capabilities

SCHEDULING WITH MULTIPLE TASKS AND ALTERNATIVE OPERATIONS:

SCHEDULING WITH MULTIPLE TASKS AND ALTERNATIVE OPERATIONS Fixed priorities (FP) heuristic: The pair with the highest priority value will be processed first Least reduction in entropy heuristic: First, the processing operation of a part with the minimum flexibility is chosen because it has the fewest choices on machines Minimum flow resistance heuristic: Priority should be given to simple heuristics that are easy to understand and implement, such as FP (for which SPT is a special case).

ECONOMIC FACTORS IN FMS SCHEDULING :

ECONOMIC FACTORS IN FMS SCHEDULING First-come-first-serve (FCFS): This rule does not consider due dates. Earliest due date (EDD): This rule assumes all orders have the same tardiness cost. Slack per Remaining Processing Time (S/RPT): This rule is derived from minimum slack(MSLACK) rule. Weighted Shortest Processing Time (WSPT): As is well known, the SPT rule provides for single machine scheduling the minimum mean flow time, which is equivalent to minimizing mean lateness Weighted Cost OVER Time (COVERT): With a look-ahead dynamic feature, it considers the expected waiting time for a remaining operation. Apparent Tardiness Cost (ATC): Also a dynamic rule, the ATC follows an exponential function of slack that involves a look-ahead parameter measured in units of average

COMPARISON OF HEURISTICS :

COMPARISON OF HEURISTICS The combined index rule with consideration of multiple parameters of system outperforms SPT and EDD rule in terms of objective function values per order. Here in we can observe that SPT as a priority index does reduce mean flow time of the system. However, since the FMS is not a single-objective manufacturing system, reducing mean flow time does not necessarily benefit the entire system. The EDD system does not consider the efficiency of the system

CONCLUSIONS :

CONCLUSIONS FMS scheduling is a difficult problem to solve optimally due to the complex nature of the system. By using well-designed heuristics, it can be rendered tractable. In facing the scheduling problem of FMS, we should notice that FMS is multi-objective, hierarchical manufacturing system with a number of manufacturing cells. Thus, it is important to consider these features when designing an FMS schedule